{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-11-21T07:08:19.086836Z",
     "start_time": "2025-11-21T07:08:19.077883Z"
    }
   },
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# Test re-load model again\n",
    "from mnist_under_pytorch_train import Net\n",
    "\n",
    "\n",
    "def test(model, device, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    all_preds = []\n",
    "    all_targets = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
    "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "            all_preds.extend(pred.cpu().numpy())\n",
    "            all_targets.extend(target.cpu().numpy())\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    accuracy = 100. * correct / len(test_loader.dataset)\n",
    "\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset), accuracy))\n",
    "\n",
    "    return accuracy, all_preds, all_targets\n",
    "\n",
    "\n",
    "def visualize_predictions(model, device, test_loader, num_images=10):\n",
    "    \"\"\"可视化预测结果，显示原始图像和识别的数字\"\"\"\n",
    "    model.eval()\n",
    "    fig, axes = plt.subplots(2, 5, figsize=(15, 8))\n",
    "    axes = axes.ravel()\n",
    "\n",
    "    # 获取一批测试数据\n",
    "    dataiter = iter(test_loader)\n",
    "    images, labels = next(dataiter)\n",
    "\n",
    "    # 将数据移到设备上并进行预测\n",
    "    images, labels = images.to(device), labels.to(device)\n",
    "    outputs = model(images)\n",
    "    preds = outputs.argmax(dim=1)\n",
    "    probs = torch.exp(outputs)  # 转换为概率\n",
    "\n",
    "    # 将图像移回CPU并转换为numpy以进行可视化\n",
    "    images = images.cpu()\n",
    "\n",
    "    for i in range(num_images):\n",
    "        # 显示图像\n",
    "        img = images[i].squeeze()\n",
    "        axes[i].imshow(img, cmap='gray')\n",
    "\n",
    "        # 获取预测概率\n",
    "        prob = probs[i].max().item() * 100\n",
    "\n",
    "        # 设置标题显示真实标签和预测标签\n",
    "        color = 'green' if preds[i].item() == labels[i].item() else 'red'\n",
    "        axes[i].set_title(f'True: {labels[i].item()}, Pred: {preds[i].item()}\\nConfidence: {prob:.1f}%',\n",
    "                         color=color, fontsize=12)\n",
    "        axes[i].axis('off')\n",
    "\n",
    "    plt.suptitle('MNIST Digit Recognition Results', fontsize=16)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T07:08:19.106086Z",
     "start_time": "2025-11-21T07:08:19.102324Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check if CUDA is available\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using device: {device}\")"
   ],
   "id": "3666490611442cd7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T07:08:21.627943Z",
     "start_time": "2025-11-21T07:08:19.117212Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create model instance\n",
    "model = Net()\n",
    "\n",
    "# Validate model exist\n",
    "if not os.path.exists(\"output/mnist_cnn.pt\"):\n",
    "    print(\"Model not found, please train first.\")\n",
    "    exit(1)\n",
    "\n",
    "# Load the model state dict with proper device mapping\n",
    "model_state_dict = torch.load(\"output/mnist_cnn.pt\", map_location=torch.device(device))\n",
    "model.load_state_dict(model_state_dict)\n",
    "\n",
    "# Move model to the appropriate device\n",
    "model = model.to(device)\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,))\n",
    "])\n",
    "mnist_test_dataset = datasets.MNIST(root=\"dataset/mnist\", train=False, download=True, transform=transform)\n",
    "test_loader = DataLoader(dataset=mnist_test_dataset, batch_size=1000, shuffle=True)\n",
    "\n",
    "# 运行测试并获取结果\n",
    "accuracy, preds, targets = test(model, device, test_loader)"
   ],
   "id": "f30efec4d2e66afc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.0272, Accuracy: 9922/10000 (99.22%)\n",
      "\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T07:08:21.638062Z",
     "start_time": "2025-11-21T07:08:21.633714Z"
    }
   },
   "cell_type": "code",
   "source": "f\"Model accuracy: {accuracy:.2f}%\"",
   "id": "f4d5d57f72bfa633",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model accuracy: 99.22%'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T07:08:22.465605Z",
     "start_time": "2025-11-21T07:08:21.663854Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可视化一些预测结果\n",
    "visualize_predictions(model, device, test_loader)"
   ],
   "id": "6b249b01e0a8d47e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
